Integrated design method and system for 3D simulation models of lighting equipment

Abstract

This invention relates to the field of lighting fixture model integration technology, and more particularly to an integrated design method and system for three-dimensional simulation models of lighting equipment. The method includes the following steps: acquiring basic data of the lighting equipment, classifying it according to components, performing three-dimensional reconstruction of independent components to form three-dimensional basic lighting fixture units, obtaining physical connection relationships, determining the functional positioning of each unit, and dividing it into three-dimensional decorative units and three-dimensional light source units; confirming the physical support structure based on the three-dimensional decorative units; optimizing the load-bearing design through weight intensification simulation to generate optimized three-dimensional decorative units; performing light scattering simulation based on the three-dimensional light source units, tracking the light scattering trajectory of the light source, optimizing the three-dimensional decorative units based on the light scattering trajectory, and integrating the three-dimensional light source units and the final decorative units to form an integrated design model of the lighting equipment. This invention improves the design efficiency of lighting equipment and promotes the digitalization and intelligentization process in the field of lighting design.

Description

Technical Field

[0001] This invention relates to the field of lighting model integration technology, and in particular to an integrated design method and system for three-dimensional simulation models of lighting equipment. Background Technology

[0002] Traditional integrated design methods for 3D simulation models of lighting equipment often rely on manual drawing and empirical judgment, resulting in low design efficiency and a lack of accuracy. Especially in complex lighting structures, the physical connection relationships between components are difficult to grasp accurately, making it impossible to achieve a systematic design approach. Traditional 3D modeling technology has significant limitations in handling dynamic light effects and structural optimization, failing to meet the modern market's demand for high-efficiency and aesthetically pleasing lighting fixtures. Furthermore, existing technologies rely heavily on simple theoretical models for light scattering simulation, lacking in-depth analysis of real light propagation behavior and failing to provide sufficient data support to ensure the lighting efficiency and visual effects of the fixtures. The load-bearing design of lighting fixtures often lacks scientific basis, potentially leading to safety hazards in practical applications. Especially in large lighting fixtures and commercial lighting scenarios, designers face multiple challenges, finding it difficult to simultaneously balance aesthetics and functionality. Summary of the Invention

[0003] Therefore, it is necessary to provide an integrated design method and system for three-dimensional simulation models of lighting equipment to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, an integrated design method for 3D simulation models of lighting equipment includes the following steps:

[0005] Step S1: Obtain basic data of lighting equipment; classify the basic data of lighting equipment according to each component to obtain data of each type of component; perform 3D reconstruction of independent components through the data of each type of component to obtain the 3D basic components of the lighting equipment;

[0006] Step S2: Obtain the physical connection relationship of the lighting equipment; determine the functional positioning of each unit in the basic three-dimensional lighting unit through the physical connection relationship of the lighting equipment; divide the basic three-dimensional lighting unit into three-dimensional decoration unit and three-dimensional light source unit based on the functional positioning of each unit;

[0007] Step S3: Confirm the physical support structure of the lamp based on the three-dimensional decorative unit; perform weight amplification simulation through the basic data of the lamp equipment, and optimize the load-bearing margin of the physical support structure of the lamp based on the simulated weight amplification, thereby generating an optimized three-dimensional decorative unit;

[0008] Step S4: Simulate light scattering from the luminaire based on the three-dimensional light source unit to generate simulated light scattering data; track the light scattering trajectory of the light source based on the simulated light scattering data;

[0009] Step S5: Optimize the light source influence of the optimized 3D decorative unit based on the light scattering trajectory of the light source to generate the final 3D decorative unit; integrate the 3D light source unit and the final 3D decorative unit to obtain the integrated design model of the lighting equipment.

[0010] This invention achieves systematic classification of various components by acquiring basic data of the lighting equipment, ensuring efficient data management and processing. The 3D reconstruction of independent components visualizes the basic building blocks of the lighting fixture, providing an intuitive foundation for subsequent design. The acquisition of physical connection relationships determines the functional positioning of each unit, ensuring the scientific and rational nature of the design. The division of 3D decorative units and 3D light source units lays the structural foundation for subsequent optimization design. The process of confirming the physical support structure of the lighting fixture, through weight intensification simulation, provides an in-depth analysis of the support structure. The implementation of the optimization design ensures the safety and stability of the lighting fixture. The generated optimized 3D decorative units enhance the overall aesthetics and functionality of the design. Based on the 3D light source units... The simulation of light scattering provides real data support for the evaluation of luminaire luminous efficacy. The generation and tracking of simulated light scattering data enables a comprehensive understanding of light source performance. The implementation of light source influence optimization ensures the optical effect of the final decorative unit in practical applications. The integrated luminaire equipment design model formed by integrating the three-dimensional light source unit and the final three-dimensional decorative unit not only improves the integrity and consistency of the design, but also provides detailed technical basis for subsequent production and application. The implementation of the overall method improves the design efficiency of luminaire equipment, shortens the development cycle, reduces costs, and promotes the digitalization and intelligentization of the luminaire design field, meets the modern market's demand for high-quality luminaires, and enhances the market competitiveness and innovation capability of luminaire products.

[0011] The present invention also provides an integrated design system for a three-dimensional simulation model of lighting equipment, for executing the integrated design method for a three-dimensional simulation model of lighting equipment as described above. The integrated design system for a three-dimensional simulation model of lighting equipment includes:

[0012] The component modeling module is used to acquire basic data of lighting equipment; classify the basic data of lighting equipment according to each component to obtain data of each type of component; and perform 3D reconstruction of independent components through the data of each type of component to obtain the 3D basic building blocks of the lighting fixture for each component.

[0013] The connection identification module is used to obtain the physical connection relationship of the lighting equipment; determine the functional positioning of each unit in the three-dimensional lighting basic component unit through the physical connection relationship of the lighting equipment; and divide the three-dimensional lighting basic component unit into three-dimensional decoration unit and three-dimensional light source unit based on the functional positioning of each unit.

[0014] The structural optimization module is used to confirm the physical support structure of the lamp based on the three-dimensional decorative unit; it performs weight aggravation simulation through the basic data of the lamp equipment, and optimizes the load-bearing margin of the physical support structure of the lamp based on the simulated weight aggravation, thereby generating an optimized three-dimensional decorative unit;

[0015] The light simulation module is used to simulate light scattering from a luminaire based on a 3D light source unit to generate simulated light scattering data; and to track the light scattering trajectory of the light source based on the simulated light scattering data.

[0016] The fusion module is used to optimize the light source effect of the optimized three-dimensional decorative unit based on the light scattering trajectory of the light source, thereby generating the final three-dimensional decorative unit; the three-dimensional light source unit and the final three-dimensional decorative unit are integrated to obtain the integrated design model of the lighting equipment.

[0017] This invention, through the implementation of a component modeling module, achieves efficient acquisition and classification of basic data for lighting equipment, ensuring the systematic nature and completeness of various component data. The 3D reconstruction of independent components provides an intuitive visual expression, facilitating in-depth analysis of the lighting structure by designers. The introduction of a connection identification module ensures accurate identification of the physical connections between the components of the lighting fixture, thus clearly defining the functional positioning of each unit. The separation of 3D decorative units and 3D light source units provides a clear structural foundation for subsequent design. The structural optimization module, through weight intensification simulation, optimizes the load-bearing capacity of the physical support structure of the lighting fixture, ensuring its safety and stability. The generated optimized 3D decorative units not only improve load-bearing capacity but also enhance overall aesthetics and practicality. The application of the light simulation module simulates light scattering from the 3D light source units. The generated simulated light scattering data provides important empirical evidence for the optical performance of the luminaire. The process of tracing the light scattering trajectory of the light source provides accurate data support for subsequent optimization of the light source's influence. The implementation of the fusion integration module realizes the effective combination of optimizing the three-dimensional decorative unit and the light source's influence. The generated final three-dimensional decorative unit improves the luminous efficacy and lighting effect of the luminaire. The design integration model formed by integrating the three-dimensional light source unit and the final three-dimensional decorative unit not only ensures the consistency and coordination of the overall design, but also provides detailed technical basis for actual production. The implementation of the overall system improves the efficiency of luminaire design, shortens the development cycle, reduces production costs, and promotes the digitalization and intelligentization of luminaire design. It meets the modern market's demand for high-quality luminaires, enhances the product's market competitiveness and innovation capabilities, and injects new vitality and impetus into the development of the luminaire industry. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the steps of an integrated design method for 3D simulation models of lighting equipment.

[0019] Figure 2 This is a detailed flowchart illustrating the implementation steps of step S2;

[0020] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0022] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0023] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0024] To achieve the above objectives, please refer to Figures 1 to 2 An integrated design method for three-dimensional simulation models of lighting equipment includes the following steps:

[0025] Step S1: Obtain basic data of lighting equipment; classify the basic data of lighting equipment according to each component to obtain data of each type of component; perform 3D reconstruction of independent components through the data of each type of component to obtain the 3D basic components of the lighting equipment;

[0026] Step S2: Obtain the physical connection relationship of the lighting equipment; determine the functional positioning of each unit in the basic three-dimensional lighting unit through the physical connection relationship of the lighting equipment; divide the basic three-dimensional lighting unit into three-dimensional decoration unit and three-dimensional light source unit based on the functional positioning of each unit;

[0027] Step S3: Confirm the physical support structure of the lamp based on the three-dimensional decorative unit; perform weight amplification simulation through the basic data of the lamp equipment, and optimize the load-bearing margin of the physical support structure of the lamp based on the simulated weight amplification, thereby generating an optimized three-dimensional decorative unit;

[0028] Step S4: Simulate light scattering from the luminaire based on the three-dimensional light source unit to generate simulated light scattering data; track the light scattering trajectory of the light source based on the simulated light scattering data;

[0029] Step S5: Optimize the light source influence of the optimized 3D decorative unit based on the light scattering trajectory of the light source to generate the final 3D decorative unit; integrate the 3D light source unit and the final 3D decorative unit to obtain the integrated design model of the lighting equipment.

[0030] This invention achieves systematic classification of various components by acquiring basic data of the lighting equipment, ensuring efficient data management and processing. The 3D reconstruction of independent components visualizes the basic building blocks of the lighting fixture, providing an intuitive foundation for subsequent design. The acquisition of physical connection relationships determines the functional positioning of each unit, ensuring the scientific and rational nature of the design. The division of 3D decorative units and 3D light source units lays the structural foundation for subsequent optimization design. The process of confirming the physical support structure of the lighting fixture, through weight intensification simulation, provides an in-depth analysis of the support structure. The implementation of the optimization design ensures the safety and stability of the lighting fixture. The generated optimized 3D decorative units enhance the overall aesthetics and functionality of the design. Based on the 3D light source units... The simulation of light scattering provides real data support for the evaluation of luminaire luminous efficacy. The generation and tracking of simulated light scattering data enables a comprehensive understanding of light source performance. The implementation of light source influence optimization ensures the optical effect of the final decorative unit in practical applications. The integrated luminaire equipment design model formed by integrating the three-dimensional light source unit and the final three-dimensional decorative unit not only improves the integrity and consistency of the design, but also provides detailed technical basis for subsequent production and application. The implementation of the overall method improves the design efficiency of luminaire equipment, shortens the development cycle, reduces costs, and promotes the digitalization and intelligentization of the luminaire design field, meets the modern market's demand for high-quality luminaires, and enhances the market competitiveness and innovation capability of luminaire products.

[0031] In this embodiment of the invention, the integrated design method for the three-dimensional simulation model of lighting equipment includes the following steps:

[0032] Step S1: Obtain basic data of lighting equipment; classify the basic data of lighting equipment according to each component to obtain data of each type of component; perform 3D reconstruction of independent components through the data of each type of component to obtain the 3D basic components of the lighting equipment;

[0033] In this embodiment, the acquisition of basic data for the lighting equipment adopts a structured acquisition method. Data is extracted from lighting design drawings, product modeling documents, and manufacturing parameter files. Vector recognition is performed on the three-view drawings using an image recognition system to identify the size, shape boundaries, and relative positional relationships of each component of the lighting fixture. The geometric construction lines and annotation lines in the drawings are identified using OpenCV image processing tools and converted into numerical structural information. Then, the triangular mesh data in the STL format part model file is parsed to extract the basic point matrix of the three-dimensional component shape. After all the data is uniformly converted into structural feature data in a unified coordinate system, the data is divided into multiple component categories such as lampshade, lamp holder, heat sink, fastener, lens, and light source installation structure according to the lamp body structure classification standard. A separate data set is established for each type of component. In the three-dimensional reconstruction stage, a voxel stitching reconstruction method based on point cloud density distribution control is used to reconstruct the volume configuration of the structural data of each type of component. The MeshLab tool is used to perform voxel fitting processing, and the three-dimensional geometric model of each component is generated by edge slicing scanning, which serves as the basic building block of the three-dimensional lighting fixture.

[0034] Step S2: Obtain the physical connection relationship of the lighting equipment; determine the functional positioning of each unit in the basic three-dimensional lighting unit through the physical connection relationship of the lighting equipment; divide the basic three-dimensional lighting unit into three-dimensional decoration unit and three-dimensional light source unit based on the functional positioning of each unit;

[0035] In this embodiment, the process of obtaining the physical connection relationship of the lighting equipment is achieved by parsing the part assembly page information in the manufacturing drawings, establishing the physical assembly path between each component using the primitive cascading connection tree method, identifying the relationship between the connector and the connected component using the parent-child structure pair in the BOM (Bill of Materials) structure table, and then performing physical connection positioning by extracting connection nodes. Through these connection information, anchor point relationships are established between each component in the aforementioned three-dimensional lighting basic unit and its connected objects. The functional area positioning of the three-dimensional lighting basic unit is performed using the spatial constraint parsing method. By calculating the direction of the gravity center line and the direction of the wire penetration between the connecting component pairs in three-dimensional coordinates, the physical function undertaken by each component in the structure is inferred. Then, the component unit that undertakes the function of supporting the structure is labeled as a three-dimensional decorative unit, and the component unit that embeds the light source or light guide structure is labeled as a three-dimensional light source unit. The structural functional partition matrix identification method is used to complete the marking operation of all units.

[0036] Step S3: Confirm the physical support structure of the lamp based on the three-dimensional decorative unit; perform weight amplification simulation through the basic data of the lamp equipment, and optimize the load-bearing margin of the physical support structure of the lamp based on the simulated weight amplification, thereby generating an optimized three-dimensional decorative unit;

[0037] In this embodiment, the operation of confirming the physical support structure of the lamp based on the three-dimensional decorative unit is reconstructed based on the three-dimensional volume distribution of the decorative unit and the contact area relationship of the components. Spatial collision detection is performed through a CAD structural analysis plugin to identify the support path with the largest contact area. Then, the component with the greatest axial stability is selected in the support path area to establish the physical support skeleton. Subsequently, in the load-bearing simulation stage, the material density parameters and geometric parameters of each unit contained in the basic data of the lamp equipment are input into the finite element analysis platform to construct a complete mechanical simulation mesh model. The self-weight aggravation factor is set to 1.25, and the self-weight load superposition simulation is performed within the spatial attitude range of the lamp to obtain the stress distribution map of the support structure. Then, based on the stress peak area in the map, the cross-sectional size is adjusted. The Beamshell hybrid modeling method is used to adjust the ratio between the surface thickness of the decorative unit and the hollow layer skeleton, thereby outputting the optimized structural configuration of the three-dimensional decorative unit.

[0038] Step S4: Simulate light scattering from the luminaire based on the three-dimensional light source unit to generate simulated light scattering data; track the light scattering trajectory of the light source based on the simulated light scattering data;

[0039] In this embodiment, the process of simulating light scattering based on three-dimensional light source units is implemented using a ray tracing algorithm. The three-dimensional light source units are input into a ray simulation platform built on the OpenGL framework. The radiation angle, wavelength range, and initial light intensity of each light source unit are set. Based on the actual parameters of the LED light source chip, the wavelength range is set to 380 nm to 780 nm, and the initial emission intensity is set to 1500 lumens. The light from the light source is emitted at a particle tracking frequency of once per nanosecond and traverses the current structural space. The collision positions, reflection angles, and refractive indices of all light rays with the surface are collected in the three-dimensional space and recorded as simulated light scattering data. Light path labels are marked for different light types in the simulated light scattering data. Based on the longest path priority rule, the main scattering paths emitted by all light sources are traced to form a complete light scattering trajectory and output as a three-dimensional vector data sequence.

[0040] Step S5: Optimize the light source influence of the optimized 3D decorative unit based on the light scattering trajectory of the light source to generate the final 3D decorative unit; integrate the 3D light source unit and the final 3D decorative unit to obtain the integrated design model of the lighting equipment.

[0041] In this embodiment, the process of optimizing the light source influence of the optimized 3D decorative unit based on the light scattering trajectory is achieved by introducing a light interference convolution kernel function for spatial influence layering. The terminal position of the scattered light is extracted from the aforementioned scattering trajectory and matched with the surface mesh points of the optimized 3D decorative unit. The unit light reception rate of each surface point is calculated. In the 3D modeling platform, the reverse mesh weighting technique is applied to fine-tune the geometric structure of the mesh area with a light reception rate higher than 85%, increasing the structural curvature to 0.45 to enhance the surface scattering guidance capability. For the area with a reception rate lower than 15%, a local excavation and chamfering operation is performed, changing the angle to a negative curvature structure to increase the reflection path density. Subsequently, the structural thickness of the light-transmitting area is adjusted by 0.2 mm to improve the light transmission coefficient, completing the output of the 3D decorative unit with optimized light source influence. Finally, the 3D light source unit and the final 3D decorative unit are integrated using Boolean operations to obtain the integrated design model of the lighting equipment.

[0042] Preferably, step S1 includes the following steps:

[0043] Step S11: Obtain basic data of lighting equipment; perform multi-dimensional attribute annotation on the basic data of lighting equipment, and deconstruct the structural hierarchy of the basic data of lighting equipment based on the attribute annotation data to obtain the component hierarchy relationship;

[0044] Step S12: Map the component hierarchy to the lighting equipment basic data, and classify the lighting equipment basic data into component categories according to the component hierarchy to obtain various component data. The number of component categories shall not be less than 4, including housing components, light source components, power supply components and control components.

[0045] Step S13: Perform 3D projection of each category using various component data to obtain the framework of each category component, wherein the 3D projection uses a voxel accuracy of 0.1-1.0mm for contour reconstruction;

[0046] Step S14: Based on the data of various components, assemble the framework of each category of components into three-dimensional units to generate the basic three-dimensional lighting fixture components of each component. The assembly size tolerance is controlled within 0.3mm, and the component volume is limited to 20-800cm². 3 between.

[0047] In this embodiment, a 3D scanner and a multimodal sensor array system are used to perform a full-structure scan of the lighting equipment. The 3D scanner must have a data acquisition rate of at least 1 million points per second and a scanning accuracy of no less than 0.05 mm. During scanning, it must rotate 360 ​​degrees around the lighting equipment to achieve uniform coverage and ensure no data obstruction areas. Simultaneously, an additional high-resolution industrial camera records texture images and establishes an RGB-depth mapping table. The acquired raw point cloud data is preprocessed, including noise reduction, registration, and resampling. Subsequently, the point cloud is initially segmented based on a component recognition algorithm. Combining the infrared and contact electromagnetic property information acquired by the sensors, multi-dimensional attribute annotations such as size, material, weight, and electrical interfaces are applied to each component. The attribute annotations are organized in key-value pairs and stored as structured tuple data. By combining this annotation data with the geometric topology of the components, a component hierarchical structure diagram is established using a graph construction method. This structure diagram contains at least three levels and clearly defines each component. The connection boundaries and assembly positions between components are determined, resulting in a component hierarchy dataset after structural deconstruction. A mapping function is established to map each level of component relationship to a specific spatial region in the original point cloud or mesh data. This mapping function is constructed based on a weighted average of spatial Euclidean distance, topological continuity, and attribute similarity. A unique identifier is assigned to each master component and embedded in the metadata index. Based on the master-slave structure identified in the component hierarchy, the index is used to classify and aggregate the original data. The aggregation process does not use global clustering but employs a condition-constrained partitioning strategy. Each component category is divided into shell components, light source components, power supply components, and control components according to their functional categories, with no data overlap allowed. An independent data set and topology information table are established for each component category. The data set stores the original content such as 3D points, textures, and attributes, while the topology information table describes the spatial positioning matrix and orientation vector of each component category. Based on the original component point cloud, Marching is then used... The Cubes algorithm is used for voxel contour reconstruction. The voxel unit side length is set between 0.1 and 1.0 mm. The voxel density is dynamically adjusted according to the complexity of the component's outer contour. If there are many curved areas in the component's shape, the side length is compressed to 0.To ensure smooth curvature transition, the reconstructed contour data is converted into triangular meshes using a polygon meshing algorithm. Independent 3D frames are established for each of the four component types. The coordinate system used during projection is fixed as a right-handed coordinate system. The meshed component frames must meet continuity checks, including mesh closure, boundary consistency, and no redundant points in the topology. The reconstruction results are stored as OBJ files with embedded attribute tables to preserve the original component annotation information. Each component type undergoes coordinate transformation using its corresponding transformation matrix to unify it under the global assembly coordinate system. Assembly operations require determining assembly reference points and alignment surfaces based on the original lighting fixture structural drawings or 3D CAD assembly constraints. Specific operations are performed using assembly plugins such as CATIA's Assembly. The Design module is used, where each assembly contact surface must satisfy three-point coplanar constraints and two vector collinear constraints to avoid redundant degrees of freedom. The assembly size tolerance is controlled within 0.3 mm. Detection is performed by scanning and reconstructing the model after assembly and calculating the minimum distance between assembly surfaces. The assembly process must ensure that the component volume is controlled between 20-800 cubic centimeters. The component's external envelope size and constraint boundaries are judged using the Brep (boundary representation) analysis function. If the volume or boundary exceeds the specified range, the component is discarded and recorded as an abnormal unit. This completes the assembly and construction of the basic 3D lighting fixture components, and saves them uniformly as a combined model data structure containing multiple data tags such as topology, attributes, and structural hierarchy.

[0048] Preferably, step S2 includes the following steps:

[0049] Step S21: Obtain the physical connection relationship of the lighting equipment; confirm the function of each connection point based on the physical connection relationship of the lighting equipment;

[0050] Step S22: Virtually project the three-dimensional lighting fixture basic component units onto each lighting fixture point in the physical connection relationship of the lighting equipment. The projection range is: projection angle less than 30°, 30° to 60°, 60° to 90°, and the projection accuracy requirement is an error range of less than 1mm.

[0051] Step S23: Map the locations of each lighting equipment based on the function of each connection point to obtain the functional positioning of each unit in the three-dimensional lighting fixture basic component unit;

[0052] Step S24: Based on the functional positioning of each unit, the basic unit of the lighting equipment is spatially divided to obtain three-dimensional decorative units and three-dimensional light source units, wherein the volume range of each unit is 1-1000 cm³. 3 The volume of the three-dimensional light source unit is 10-500 cm³. 3 .

[0053] In this embodiment, a structural sensing matrix and an electromagnetic induction array are used to jointly collect the connection structure information of the lighting equipment. The structural sensing matrix is ​​arranged at the connection interface of each component and collects the structural constraint boundary of the connection part through stress response signal. The electromagnetic induction array is used to sense the electrical signal transmission path and contact connection structure between components. The collected data includes the center coordinates of the connection surface, normal vector, connection type code, electrical contact state and fastening strength level. The acquisition accuracy requires that the spatial coordinate error does not exceed 0.3 mm. The electrical signal identification frequency range is set to 1kHz to 20kHz to distinguish the high and low frequency connection states of the control component and the power supply component. Subsequently, the connection data is structured by a graph neural network model based on point group and structural topology analysis. The process identifies the function of each connection point, categorizing it into six functional labels: power input, electrical signal control, neutral connection, mechanical fixation, heat dissipation, and decorative connection. Functional confirmation is achieved by assigning a functional vector to each connection point. A unified assembly space scene is constructed using a virtual coordinate mapping module. The 3D components are initially placed at the origin of the world coordinate system, and view frustum projection simulations are performed sequentially towards each physical connection point. Three view frustum models are set with projection angle ranges of less than 30 degrees, 30 to 60 degrees, and 60 to 90 degrees, generating three sets of virtual projection paths. Each view frustum model uses the connection point as the projection target endpoint and the center of the component as the projection source point, establishing a direction vector field. Affine transformation and vector rotation matrices are used to calculate the direction vector field for each connection point. During the projection process, the rotation angle and displacement distance of the component are measured in real time between the component unit and the target point, ensuring that the error range is less than 1 mm. If the error exceeds 1 mm, the component is considered a projection failure, and the rotation reference point of the source component is readjusted. The relative pose matrix of all successfully projected components is recorded and stored in JSON format for subsequent function positioning. The function action vector is called and combined with the 3D projection results to generate a function mapping matrix. This matrix establishes a one-to-one functional association between each structural unit in the component unit and its corresponding connection point. During the mapping process, the spatial centroid distance and function vector similarity are used as the main matching criteria to divide the mesh segment inside each component unit into minimum functional regions. The K-means clustering algorithm is used to cluster geometric fragments of the same functional category and identify unique functional domain numbers. For example, the heat-generating areas corresponding to the light-emitting components of the light source need to be clustered within the same number and mapped to power control points or heat conduction points. At the same time, the functional positioning is further refined by the electrical characteristics of the connection points, and the three-dimensional positions of the control interface, power supply interface, and grounding port are marked. The attribute fields of the constituent units are updated through this functional mapping matrix to form clear functional partition labels and embed them into the corresponding triangular mesh structure to form the basic constituent units of the 3D lighting fixture after functional positioning. The three-dimensional Boolean operation and spatial voxel clipping module is called to perform geometric decomposition of the constituent unit model. First, the cutting boundary volume is generated according to the functional number identified in the functional mapping.The structure is a spatial convex hull structure based on functional boundaries. Then, using this boundary volume as a template, Boolean clipping operations are performed on the original mesh model. During clipping, Boolean difference and intersection operations are used to generate independent functional substructures. All substructures are named and categorized according to their functional numbers. All optically related functional substructures are uniformly grouped into the 3D light source unit set, while other functional substructures are grouped into the 3D decoration unit set. Subsequently, a volume calculation operation is performed on each segmented unit, using the method of subtracting the cavity volume from the bounding box volume to calculate the actual volume of the unit component. The volume of the 3D decoration unit is limited to between 1 and 1000 cubic centimeters, and the volume of the 3D light source unit is limited to between 10 and 500 cubic centimeters. If the volume exceeds the limits, it is further divided into blocks using a re-segmentation algorithm. The final output spatial segmentation result is a set of 3D models with functional labels, spatial coordinates, and volume parameters.

[0054] Preferably, step S3, which confirms the physical support structure of the lamp based on the three-dimensional decorative unit, includes:

[0055] Identify the unit geometry of three-dimensional decorative elements;

[0056] Fit the unit geometry to the three-dimensional edge contour of the decorative unit;

[0057] The three-dimensional edge contour is locally sectioned to obtain the decorative unit section frame;

[0058] The centroid data of the three-dimensional decorative unit are analyzed based on the cross-sectional frame of the decorative unit.

[0059] Extract material composition data from the 3D decorative unit;

[0060] Material distribution simulation is performed based on material composition data and decorative unit cross-sectional framework to generate decorative unit material distribution data;

[0061] The unit centroid data and the decorative unit material distribution data are coupled and processed to infer the mechanical centroid of the decorative unit;

[0062] Gravity conduction is traced by the mechanical center of gravity of the decorative unit to generate the force conduction path of the lamp;

[0063] Reconstruct the physical support structure of the luminaire based on the force transmission path of the luminaire.

[0064] In this embodiment, when identifying the unit geometry of a 3D decorative unit, it is necessary to perform surface clustering based on the preprocessed 3D triangular mesh model. A region growing algorithm is used to aggregate surfaces with continuous curvature. Initial surface segmentation is achieved by setting the average curvature threshold between 0.015 and 0.03. Then, the principal direction vector analysis module is called to extract the principal normal vector direction of each surface, and similarity comparison is performed on adjacent surfaces. If the angle between the principal directions is less than 15 degrees, they are merged into the same geometric fragment. The merged surface then undergoes shape factor extraction processing, calculating parameters such as principal axis length, maximum circumscribed sphere diameter, average concavity / convexity index, number of boundary polylines, and number of facets. Finally, the identified geometric shapes are labeled with structural tags such as convex spherical surface, freeform surface, cuboid surface, and surface of revolution, and these geometric tags are embedded into the 3D triangular mesh model. In the model nodes of the decorative unit, when fitting the unit geometry to the 3D edge contour of the decorative unit, it is necessary to first construct the boundary point set of the unit surface. By traversing the edge mesh of each region, the endpoints of the edge line segments are extracted and a sparse boundary point set is formed. Then, the point set is fitted with the minimum boundary curve. Catmull-Rom interpolation splines are used to generate continuous boundary curves. The connection relationship of the endpoints of each boundary curve is processed by topological closure to form a closed edge contour structure. Subsequently, the closed curve is subjected to 3D projection processing. The edge contour is projected onto the three principal planes XY, YZ, and XZ respectively. A multi-view contour set is established and fused into a spatial edge contour mesh according to the view reconstruction rules. The contour mesh is discretized with dense curves, and the number of edge control points is not less than 200. The curvature difference between each control point is controlled to be 0.Within 0.02, to ensure the spatial continuity and shape fidelity of edge fitting, when performing local sectioning on the 3D edge contour, first, a sectioning direction vector perpendicular to the local surface is set based on the center point of the edge contour. Then, a scanning section volume is constructed with the sectioning vector as the central axis. The scanning section volume is a set of equally spaced parallel planes with a spacing of 1 mm to 3 mm. A section line segment is generated at the intersection of each section plane and the 3D edge contour. Section contour fragments are constructed through control points on each line segment. The Bezier interpolation algorithm is used to smooth all section contours, and finally, a section frame is generated. The frame, or cross-sectional framework, contains a hierarchical structure. The cross-sectional profile of each layer maintains a 1 mm interpolation consistency with adjacent layers to ensure the continuity of the 3D structure after sectioning. The output cross-sectional framework is stored in OBJ format for subsequent structural analysis. When analyzing the centroid data of the 3D decorative elements based on the cross-sectional framework, a 3D mesh model must first be constructed based on the cross-sectional framework. Volume blocks are generated by sweeping and reconstructing each cross-sectional profile. The volume block accuracy is controlled to one voxel per cubic millimeter. A 3D integral calculation is performed using the assumption of uniform mass distribution, considering the spatial distribution of each voxel. The coordinates are averaged and weighted to obtain the spatial geometric centroid coordinates of the overall structure. The centroid coordinates are represented by three-dimensional vectors, representing the positional distribution of the centroid along the X, Y, and Z axes, respectively. After the centroid calculation is completed, this data is injected into the attribute description node of the decorative unit for subsequent centroid modeling operations. When extracting the material composition data of the three-dimensional decorative unit, an X-ray fluorescence spectrometer is used to perform non-contact analysis on the material information in the solid model or simulation material model. The test parameters are set as follows: voltage 50kV, current 150μA, exposure time 120 seconds, and scanning spot diameter 1 mm. After obtaining the elemental spectral data of the outer shell and internal filling part of the decorative unit, the material database is called for elemental comparison and matching. The identified material types include ABS engineering plastic, PC polycarbonate, aluminum alloy 6061-T6, stainless steel 304, etc. The proportion of each material in the unit model is recorded and expressed as a percentage of the volume distribution ratio of different materials in space. When simulating the material distribution based on the material composition data and the cross-sectional frame of the decorative unit, a spatial material distribution voxel mesh is constructed, with each voxel unit having a resolution of 0.The mesh boundary is defined by a 5mm profile frame. Material composition data is used as a weight input for the material filling mark of each voxel unit. The material filling process is simulated by a three-dimensional Gaussian random field. The material aggregation area is controlled by setting different spatial bias functions for different materials. For example, ABS engineering plastic is aggregated in the outer layer and the thickness is set to 2mm. Aluminum alloy is set as the internal structural skeleton and distributed around the centroid. A unique material mark is generated in each voxel unit to form a complete material distribution map. The output format is a three-dimensional matrix, and each coordinate point corresponds to a specific material type number. When coupling the unit centroid data and the decorative unit material distribution data and inferring the mechanical center of gravity of the decorative unit, the position of the mass center point of each voxel unit needs to be calculated based on the three-dimensional material distribution voxel mesh and integrated by mass weighting. The mass calculation is based on the density parameter of each material. For ABS, it is set to 1.04g / cm³. 3 The aluminum alloy is set at 2.7g / cm³. 3 Stainless steel is set at 7.9 g / cm³. 3 The PC material is set at 1.2 g / cm³. 3 A mass-weighted 3D integral operation is performed on all voxel units, and the result is mapped to a 3D space centroid vector. This vector represents the mechanical centroid position of the decorative unit. The generated mechanical centroid data structure is consistent with the centroid data structure format. When tracking gravity transmission through the mechanical centroid of the decorative unit, a gravity vector field is constructed and projected downwards from the centroid to build a gravity path. The gravity direction is set to the -Z axis, and the unit gravitational acceleration is 9.8 m / s². 2 Using a three-dimensional material distribution mesh as the path propagation medium, a voxel force flow analysis algorithm is employed to simulate the transmission path of gravity in each material element. Material stiffness is considered in the path calculation; the stiffness of ABS is set to 2.1 GPa, and that of aluminum alloy is set to 70 GPa. Stiffness affects path offset. Each path node records its spatial coordinates, force transmission angle, force loss coefficient, and connection relationship with the next level path, ultimately generating a complete force transmission path map. When reconstructing the physical support structure of the lighting fixture based on its force transmission path, path termination points are identified, and path aggregation areas are extracted as physical support nodes. Each support node is then further processed... The shortest structural connection is used to construct a support skeleton model based on the topology. The support structure is composed of circular tube beam elements with a diameter of 10 mm. The material used is structural steel Q235, and the Young modulus is set to 200 GPa. The layout of the support structure must meet the structural stability conditions. Static simulation is performed on the reconstructed support structure. The load is set as the sum of the weight of the decorative elements multiplied by a safety factor of 1.5. The support structure is deemed effective after the maximum displacement is less than 0.5 mm in the simulation. The final output is a three-dimensional solid model file of the physical support structure of the lighting fixture, which is then imported into the subsequent simulation module to complete the structural integration modeling.

[0065] Of particular importance is the coupling of unit centroid data and decorative unit material distribution data to infer the mechanical centroid of the decorative unit, including:

[0066] Spatial node pairing is performed between the unit centroid data and the decorative unit material distribution data to obtain centroid material node data;

[0067] Attribute weight fusion is performed based on the centroid material node data to obtain weighted fused node data;

[0068] Spatial coupling mapping is performed based on weighted fusion node data to obtain coupled distribution data;

[0069] The coupled distribution data is processed by principal axis projection to obtain principal axis projection data;

[0070] By inferring the center of gravity offset from the main axis projection data, the mechanical center of gravity data of the decorative unit is obtained.

[0071] In this embodiment, the unit centroid data is loaded into the node grid coordinate system in the form of a three-dimensional vector. Each centroid node coordinate includes X, Y, and Z axis values ​​in millimeters. Simultaneously, the decorative unit material distribution data is parsed into a voxel-level three-dimensional matrix structure. Each voxel unit contains spatial location coordinates, material number, and material density value. First, a spatial KD tree index structure is established to quickly retrieve the nearest neighbor position of the centroid node in the material voxel grid. Spatial overlap detection is performed on each centroid node and the material nodes within its surrounding 3×3×3 voxel region. If a centroid node is located within 3 millimeters of the center of a material voxel, that material voxel is marked as being adjacent to that centroid. Node pairing is successful, resulting in a set of centroid material node data. Each set of data includes a three-dimensional position vector, the corresponding material number, and the local density information at that point. An attribute set is defined for each set of centroid material nodes, including node coordinates, material density values, local material distribution gradient values, and the structural stiffness index of adjacent voxels. Structural stiffness is input using the Young's modulus of the material: 2.1 GPa for ABS, 1.2 GPa for PC, 70 GPa for aluminum alloy, and 195 GPa for stainless steel. After normalization, density is assigned the primary weight: 0.5 for density, 0.3 for structural stiffness, and 0 for material gradient.2. A linear weighted fusion formula is used to construct a single physical weight for each fusion node. This weight, combined with the corresponding spatial coordinates, forms a weighted fusion node dataset. The dataset structure is a triple vector, including spatial coordinate vectors and weight scalars, used in the subsequent spatial field modeling process. A three-dimensional coupling vector field is constructed, with the boundary of the entire decorative unit as the coordinate system. All weighted fusion node coordinates are interpolated and embedded into this coordinate system. The interpolation method used is the Inverse Distance Weighting (IDW) interpolation method, with the weight influence radius set to 10 mm. For each spatial coordinate point, the weighted values ​​of its eight surrounding weighted fusion nodes are calculated, and a local field function is constructed. Each field function result is mapped to a coupling density value, which represents the spatial coupling strength of the influence of the centroid density and material stiffness on that coordinate point. The final constructed coupling distribution data is a three-dimensional continuous scalar field, with each coordinate point containing a coupling density value. This data structure is stored in the form of a dense voxel mesh, with the voxel precision set to 1 mm. The overall mesh size is based on the boundary of the decorative unit. The box automatically adjusts and performs PCA principal component analysis on the 3D coupled distribution data field. The first principal axis direction of the distribution data is extracted as the principal projection direction. This principal axis is calculated using the direction of maximum variance as the reference. Then, the entire 3D coupled distribution data is orthogonally projected using the principal axis as the reference line. The projection result is a one-dimensional continuous vector density field. A density distribution function is constructed on this vector density field, with the position along the principal axis as the independent variable and the coupling density value at the corresponding position as the dependent variable. The mean distribution value of the coupling density along the principal axis is obtained through numerical integration. This mean value position is the initial mechanical center trend point after projection. The region of maximum density concentration and boundary limit points are recorded for subsequent offset calculations. The principal axis projection data is stored in array form, recording the density value and corresponding spatial coordinates of each principal axis position point. The position of the mass center point along the principal axis is determined based on the projected density distribution function. This is achieved by integrating the density function over the principal axis length and then using a density-weighted average to determine the position of the integral result, which serves as the initial centroid along the principal axis. Next, local second-order difference processing is performed on the region of maximum density concentration in the projection data to identify the density change rates on the left and right sides. If the density increment on the left side is higher than the right side by more than a threshold of 0.1, it indicates a centroid offset. The initial center of gravity position is then corrected and offset along the principal axis. The offset amount is determined by the local density asymmetry function, and the offset formula is Δx = α × (ρL - ρR) / (ρL + ρR), where ρL and ρR are the average densities on the left and right sides of the center of gravity, respectively, and α is set to 0.02 of the principal axis length. The final offset correction result is output as the mechanical center of gravity coordinates of the decorative unit. This mechanical center of gravity data structure is a three-dimensional vector, corresponding to the mechanical equilibrium point positions in the XYZ axis directions, and is stored in the data interface of the structural analysis module for calling the gravity path analysis function in the subsequent simulation module.

[0072] Preferably, step S3 involves simulating the increased weight using basic data of the lighting equipment, and optimizing the load-bearing capacity of the physical support structure of the lighting fixture based on the simulated increased weight, including:

[0073] Obtain the weight data of the lighting fixture's additional components;

[0074] Extract the overall weight data of the lighting fixtures from the basic data of the lighting equipment;

[0075] By using the weight data of the lamp's additional components to simulate the overall weight of the lamp, the simulated weight increase is obtained.

[0076] Stress response simulation is performed on the physical support structure of the lighting fixture to generate load-bearing capacity data of the lighting fixture support structure;

[0077] Interactive analysis is performed based on simulated weight and load-bearing capacity data to obtain the load-bearing capacity margin.

[0078] Based on the load-bearing capacity margin, the physical support structure of the lighting fixture is optimized to generate an optimized three-dimensional decorative unit.

[0079] In this embodiment, all non-core light source components in the 3D decorative unit model are identified and numbered one by one. This identification process is based on the classification and layering of component attribute tags in the model. All model nodes marked as "auxiliary structure" or "auxiliary decoration" are extracted to establish a separate data layer. The geometric model of each additional component is volume calculated through a 3D CAD modeling interface using the VolumeProperty function in the SolidWorks API interface, with the volume unit set to cubic millimeters. Subsequently, according to the material label of the additional component, the corresponding material density value is read from the material database. The material database is in CSV format and includes parameters such as material number, density value, Young's modulus, and Poisson's ratio. For example, the density of ABS is 1040 kg / m³, the density of PC is 1210 kg / m³, and the density of aluminum alloy is 2700 kg / m³. The volume value is multiplied by the density to obtain the weight value of each additional component. Then, the weights of all components are summed to form the weight data of the lighting fixture's additional components. The weight is stored as a floating-point array, with each value representing the weight of a component in grams. The parameter configuration interface of the 3D lighting fixture's basic model is called. This interface is defined in the extended attribute node of the STEP model file. Basic data fields include the main structural dimensions of the lamp body, material identification, design weight, and center coordinates. The original weight value of the entire lamp structure is obtained by parsing the "DesignWeight" field in the basic model. This weight value originates from the static load calibration data during the design phase and is stored in kilograms. The extraction of this field uses Python in conjunction with the openSTEP library for structural analysis. The structure fields are screened using regular expressions. The numerical parts of the matching results are converted to floating-point numbers and then uniformly converted to grams and stored in a temporary buffer of the lighting equipment's basic data file. The data obtained in this step will be used to simulate the initial load. All elements in the aforementioned additional component weight array are summed to form a total additional weight value. Then, the total lamp weight value in the basic data is summed with this additional weight to form the simulated increased weight. This calculation operation is performed using the array summation function and floating-point addition operator in the NumPy math library. The simulated increased weight data is in grams, and the numerical precision is retained to two decimal places for subsequent applications. The force simulation loading provides a standard mass input value, and simultaneously embeds this simulated weight as a concentrated load onto the mass point of the lighting fixture model. The position of this mass point is provided by the coordinate values ​​determined in the aforementioned mechanical center of gravity step, specifically expressed in XYZ three-dimensional space. The simulated weight is applied as a concentrated mass to this node to participate in the structural stress simulation. A finite element analysis model of the supporting structure is established, and HyperMesh is used to mesh the structural model in three dimensions. The mesh element type is set to tetrahedral elements, and the element size is set to vary from 2 mm to 4 mm. A dense mesh of 2 mm is used in local detailed areas, and a mesh of 4 mm is used in coarse structural areas.Material properties are assigned based on the aforementioned material database. Each structural unit will be marked with a unique material number and associated material properties, including Young's modulus, Poisson's ratio, and yield strength. The simulation boundary conditions are set as follows: the lower support surface is completely fixed, and a load is applied to the upper mechanical center of gravity node in the negative Z-axis direction. The load value is equal to the gravity value corresponding to the simulated weight increase. The simulation software used is ANSYS. The Mechanical module performs static analysis, solving for the stress, displacement, and safety factor values ​​for each structural node. The results are exported in .vtk format and converted into a structural load-bearing capacity dataset. The simulated nodal stress values ​​are compared with the material yield strength of the structural elements. Matplotlib is used to plot stress distribution maps with overlaid contour layers. Pandas is then used to construct a data table for regions where stress exceeds the critical value. Each table item includes the element number, maximum stress value, critical stress ratio, and structural location coordinates. Simultaneously, the simulated added weight is proportionally mapped to each node to form an equivalent nodal load model. Cross-analysis is performed by comparing the actual load-bearing capacity of each node with the added weight distribution to generate load-bearing margin data. This data is a three-dimensional safety factor distribution map, with regions having a safety factor less than 1 indicating... The load-bearing capacity is insufficient; the area between 1 and 1.5 is considered a low safety margin region, while the area greater than 1.5 is considered a load-bearing region. All node data will be output in JSON format for model input optimization. A local optimization target domain is established based on the coordinate position of the low margin region. A shape optimization algorithm is used to adjust the geometric structure. The optimization objective function is constructed using the topology optimization tool in the OptiStruct module, with the minimum structural mass as the constraint objective function and the maximum structural stress not exceeding the material yield limit as the equality constraint condition. The material distribution pattern is controlled by the element removal rate in the target region. The number of optimization iterations is set to 150, the material removal threshold is set to 0.05, and the shape change accuracy is 0.1 mm. During the optimization process, the residual decreasing trend is monitored to ensure optimization convergence. Finally, the optimized structural topology layout is obtained. This structure will be returned to the 3D modeling platform, and the reconstruction modeling operation will be completed through the CATIA V5 modeling interface. The retained areas in the topology optimization results will be converted into specific solid geometry, and fillet treatment and assembly reserved holes will be added to form a new 3D decorative unit geometric model. The output format is a .STEP standard model file and marked with the optimization version number.

[0080] Of particular importance is the interactive analysis based on simulated weight and load-bearing capacity data to obtain the load-bearing requirement margin, including:

[0081] Node alignment processing is performed on the simulated weight and load-bearing capacity data to generate node weight comparison data;

[0082] Local load distribution screening is performed based on node weight comparison data to obtain distribution screening data;

[0083] Mark the extreme states of the distribution screening data and generate extreme state data;

[0084] Calculate the load-bearing margin of the limit state data to obtain the load-bearing margin data;

[0085] By integrating the load-bearing margin data across the entire domain, the load-bearing requirement margin can be obtained.

[0086] In this embodiment, the two types of data are subjected to three-dimensional spatial node coordinate normalization. Specifically, the grid node coordinates of the physical support structure of the lighting fixture are used as a spatial reference benchmark. A three-dimensional point set alignment algorithm (PointCloud Registration) is employed, and the ICP (Iterative Closest Point) algorithm is used to map the load-bearing capacity data with the simulated intensified weight loading points. The mapping operation must ensure that each node applying the load maintains a maximum Euclidean distance of no more than 0.1 mm from the corresponding node in the load-bearing result. All node coordinate data are uniformly converted to millimeters and retained to three decimal places. A node ID binding mechanism is used to establish a one-to-one correspondence between the intensified weight data and the stress-strain data, generating a node weight comparison data structure. Each node structure contains six fields: node number, three-dimensional coordinates, simulated weight, node stress value, material yield strength, and normalized weight ratio. The normalized weight ratio is the ratio of node stress to yield strength. The node group is clustered using a spatial region clustering algorithm. The clustering method adopted is DBSCAN (Density-Based Spatial Clustering of Applications with The Noise algorithm (density-based spatial clustering) sets the minimum neighborhood distance to 5 mm and the minimum number of cluster cores to 12. It identifies localized load anomaly regions within each cluster based on normalized weight ratios. If more than 30% of the nodes in a cluster have a weight ratio greater than 0.8, that region is marked as a high-load region. All nodes in each high-load region are renumbered and formed into independent data groups. During the screening process, isolated data points and marginal discrete nodes are removed to eliminate noise. The screening results are represented by the high-load cluster number, number of nodes, average weight ratio, maximum weight ratio, and spatial bounding box. This distributed screening dataset is merged and stored in JSON format. The data structure retains all node numbers and their weight ratios within each cluster region. Each cluster in the screening data is extracted separately and a limit judgment condition is set. The limit state is marked when the normalized weight ratio of the nodes exceeds 0.A node is marked as a limit state node if its stress value is greater than or equal to 95% of the material's yield strength. All limit state nodes will be displayed as red nodes in the 3D visualization tool Paraview. Each limit state node will have additional fields recording its cluster number, local maximum stress, weight value corresponding to the loading point, and its position offset vector. All limit state nodes will be output in CSV table format for load margin calculation. The table fields include: node ID, X coordinate, Y coordinate, Z coordinate, stress value, yield strength, normalized weight ratio, cluster region number, and limit state. The remaining load capacity Δσ(delta) is calculated by subtracting the stress value of each limit node from its material yield strength. The load factor α is calculated by applying a simulated aggravated load (sigma) to each node. Then, the load factor α is calculated using the concentrated load distribution weight along the Z-axis. The local node load margin is then calculated by dividing the remaining load capacity by the load factor, expressed numerically as: load margin equals remaining load capacity (MPa) divided by the load factor (N). All node load margin values ​​are aggregated to form a load margin dataset. Each data record contains a node ID, cluster number, remaining load capacity, load factor, and load margin value. All cluster numbers are iterated through, and the load margin values ​​within each cluster are weighted by the number of nodes to obtain the regional load margin value. Finally, all clustered regions are sorted by spatial location. The data was reconstructed into a complete 3D mesh mapping structure to form a spatial distribution map of the overall load-bearing margin. A 3D color distribution map was generated using the Matplotlib and Mayavi libraries in Python. The map uses RGB color intensity to indicate the local load-bearing margin level, with blue representing high margin and red representing near-limit state. Subsequently, the load-bearing margin values ​​of each region were averaged to generate overall statistical indicators, including average load-bearing margin, minimum load-bearing margin, maximum load-bearing margin, and the percentage of low-margin areas. Finally, all load-bearing margin statistics and 3D distribution map data were integrated to form a load-bearing demand margin structure. The structure fields include the overall average, the number of abnormal areas, the coordinates of the minimum load-bearing margin point, the percentage of low-margin nodes, and a list of suggested optimization area numbers.

[0087] Preferably, step S4, which involves simulating light scattering from a luminaire based on a three-dimensional light source unit, includes:

[0088] Constructing the light emission domain of the light source based on three-dimensional light source units;

[0089] Light scattering nodes are traced based on the light emission domain of the light source to generate multi-directional light scattering nodes;

[0090] The luminous intensity of the light source's emission domain is encoded to obtain the luminous intensity distribution code;

[0091] By embedding the luminous intensity distribution code into the multi-directional light scattering node, simulated light scattering data is obtained.

[0092] In this embodiment, the process of constructing the light source emission domain based on the three-dimensional light source unit relies on the geometric model of the light source unit already established in the three-dimensional modeling platform. This geometric model needs to include the actual spatial coordinates of the light source shell structure, the position of the emission surface, and the internal emission core. By calling the light source construction function in the Photopia module of the optical simulation platform, the light source radiation morphology parameters contained in the light source model are read. The radiation morphology parameters need to include the luminous flux unit (in lm), the emission angle range (set separately in the horizontal and vertical angular directions, in degrees), and the emission starting plane (in mm). After completing the parameter configuration, a spatial mesh algorithm based on the BSP (Binary Space Partition) tree structure is used to perform volume subdivision of the entire light source emission envelope region. The subdivision spacing threshold needs to be set to be less than 5 mm to ensure the high-density light source emission path recording accuracy. Finally, all envelopes in the subdivision region are converted into a light source emission domain voxel dataset. This dataset is saved in a structure format, and each voxel node records the emission starting position, emission direction vector, and spatial normal direction triplet. By calling Monte The Carlo path tracing module uses the voxel dataset of the light source's emission domain as the initial path set, setting the initial total number of light rays to 100,000. Each path employs an independent random ray generation algorithm, which generates an initial direction vector for each voxel point. The offset angle range is controlled within 0 to the maximum emission angle boundary. A vector rotation matrix is ​​used to perturb the ray direction, ensuring uniform distribution of light rays in different directions. During path tracing, the internal structural boundary model of the luminaire needs to be loaded. This boundary model is constructed in STL format, and the BVH (Bounding Volume Hierarchy) algorithm is used for accelerated collision detection, detecting the intersection points of each light ray path with the boundary. For each intersection point, a scattering node is established, recording parameters such as incident direction, collision normal, refractive index, scattering angle, and reflectivity. Each light ray path generates a maximum of 15 scattering nodes. Finally, all scattering nodes are archived into a multi-directional light scattering node list according to their path numbers. The luminous intensity distribution map data from the original luminaire emission parameters is retrieved. This data is a luminous intensity matrix recorded in spherical coordinates, with its latitudinal dimension ranging from 0 to 3 degrees horizontally. The data is calculated using a 60-degree longitude coordinate system, with longitude ranging from 0 to 180 degrees vertically. Each direction node corresponds to a light intensity value in cd (candela). This data is converted into a three-dimensional light intensity tensor in a Cartesian coordinate system, with the tensor dimensions set to 100×100×100. A corresponding spherical light intensity value is matched to each voxel location, and intermediate values ​​are filled using a spherical interpolation algorithm to ensure that each voxel has a continuous light intensity distribution. This intensity value is then linearly normalized to maintain its range between 0 and 1, and finally quantized using 8-bit binary encoding.Each voxel generates an 8-bit luminous intensity code. This luminous intensity code is embedded in the node data structure as additional information for each node in the light source's emission domain, forming a luminous intensity distribution coding matrix. Each scattering node in the multi-directional light scattering node list is matched. Based on the spatial coordinates of each scattering node and the direction vector of its previous hop node, the voxel data of the light source's emission domain is traced backwards to identify its initial starting position. The 8-bit luminous intensity code in this initial voxel is used as the initial luminous intensity factor of the scattering node. Then, combined with the reflectivity and scattering angle data in the scattering node, the initial luminous intensity factor is attenuated using a decay function. The luminous intensity attenuation function is set as I' = I0 × cosθ × R, where I0 is the original luminous intensity factor, θ is the scattering angle, and R is the reflectivity of the scattering surface. After all scattering nodes are corrected by the above attenuation function, the additional field I' in the node is updated, forming a complete simulated light scattering data list. Each node in this list contains spatial position, incident direction, exit direction, refractive index, scattering angle, reflectivity, and final luminous intensity value. This dataset can fulfill the light scattering information configuration requirements of the light simulation module in the lighting simulation model.

[0093] Preferably, step S4, which involves tracing the light scattering trajectory of the light source based on simulated light scattering data, includes:

[0094] The starting point of the simulated light scattering data is marked to obtain the starting label of the scattered light;

[0095] Based on the structured light path vector of the scattered light starting tag, an initial light path vector is generated;

[0096] The energy transfer path is obtained by mapping the energy transfer between scattering nodes of the simulated light scattering data using the initial vector of the light path;

[0097] The energy transfer path data is fused and recombined to generate a fused scattering path;

[0098] The temporal trajectory is reconstructed based on the fused scattering path, thereby obtaining the scattering trajectory of the light source.

[0099] In this embodiment, a simulated light scattering data matrix containing the coordinates of each scattering node in three-dimensional space, the emission angle, and the unit energy density information is loaded. This data matrix is ​​generated in the previous simulation stage and stored in a structured manner as a multi-dimensional coordinate array. Utilizing coordinate constraints based on three-dimensional Euclidean space, a volume labeling algorithm oriented towards voxel tracking is invoked. A minimum spherical structure is established in the simulation space according to the geometric center of the light source unit. Using all photon units radiating outward from the surface of this structure as a reference, the spatial distance between the initial emission angle and the starting point is determined point by point to be lower than a set threshold d0, where d0 is 0.25 mm. Through a custom labeling logic, each node that meets the conditions is assigned an identifier code tag_start, and its corresponding emission direction vector and energy unit are bound as an independent structure field. This outputs a scattered light starting tag data file containing tag_start information and performs indexing. The sorting process involves vectorizing the initial label data output from the previous step. First, a 3D vector representation is constructed based on the coordinate field and its associated direction vector field of each initial label node. This representation is then converted into a column vector matrix V_start, where each column represents the spatial emission direction of a ray. Next, the coordinate position matrix C_start is multiplied element-wise with V_start using a weighted multiplication. The displacement vector ΔX of each point at unit time t=1 is calculated using a standard linear combination structure. Simultaneously, the initial energy E0=1w is set based on the energy attenuation rate η of each unit voxel's path, constructing an initial vector structure set. Each element contains the initial coordinates, direction vector, unit step size, energy attenuation function, and current state flag field state_flag. Finally, this structure set is exported as the initial vector file for the light path and written to the simulation database in JSON format as the input source for subsequent path tracing. This is based on Monte Carlo simulation. The Carlo ray tracing method constructs the energy transfer mapping process between nodes. First, each structure instance in the initial vector file is used as the particle source emission base point. By calling the path stepping function in the ray tracing module, ray propagation is simulated in the three-dimensional voxel mesh space. The propagation distance Δd is fixed at 0.1 mm per step, depending on the light propagation direction vector. At the current node, it searches for matching nodes in the simulated ray scattering data that have overlapping coordinates or a distance less than the error tolerance ε. If a matching node is found, an energy transfer operation is performed, attenuating the remaining energy of the previous node by a scaling factor η and transferring it to the current node. The source node, target node, attenuation factor, and transferred energy are recorded to construct a set of energy transfer event quadruples. This process is continuously iterated until the current energy value is lower than the threshold E_min = 0.When the maximum number of jump steps N_max = 100 is reached, all energy transfer events are stored as a directed graph structure, with nodes as vertices and energy transfer paths as edges. A complete energy transfer path graph G_energy is output. After construction, an index file is generated for path fusion. From the G_energy graph exported in the previous stage, all directed paths with the same starting point tag_start as the source are extracted. A weighted aggregation algorithm is used to merge multi-branch paths with the same source. Dijkstra's shortest weighted path algorithm is used as the core mechanism for path fusion, where the edge weight is the transmitted energy after unit energy decay. The fused path must satisfy the condition that the cumulative energy is not less than the set critical threshold E_thresh = 0.05w. After path fusion, an aggregated path structure is constructed for each group of light source nodes. All nodes and direction vectors in the path are sequentially concatenated, while tail fragment path segments with energy less than E_drop = 0.005w are removed. Finally, a serialized data structure containing each fused path is output. This data structure includes the path number, Key fields such as path node sequence, direction sequence, cumulative energy sequence, and path hop count are imported into the trajectory reconstruction module via a unified data interface as input parameters. Utilizing the node sequence and direction vector sequence from the fused path structure, an equally spaced temporal interpolation operation is performed using a path interpolation function, with a time step t_step set to 1 millisecond. First, the total path length L_path is calculated, and the number of interpolation points N_interp is determined based on L_path and t_step. A three-dimensional vector linear interpolation algorithm is used to temporally interpolate the original path nodes. Each interpolation node includes spatial coordinates, direction vector, timestamp, and energy value fields. Based on this, a forward differential velocity estimation model is introduced to continuously model the path change trend, and a fifth-order B-spline fitting algorithm is used for temporal trajectory smoothing. The constructed temporal trajectory structure includes path ID, start time, end time, path point sequence, mean of fitting residuals, and total energy value. Finally, the light scattering trajectory of the light source is stored in HDF5 format in the simulation results directory.

[0100] Preferably, step S5, which optimizes the light source influence of the optimized 3D decorative unit based on the light scattering trajectory of the light source, includes:

[0101] By combining the light scattering trajectory of the light source and optimizing the three-dimensional decorative units, they are projected into the same space to obtain a combined projection frame;

[0102] Based on the identification of light scattering trajectories from the light source using a combined projection frame and the optimization of the overlapping projection areas of the three-dimensional decorative units;

[0103] Optimize the light-blocking decorative area in the 3D decorative unit by mapping the overlapping projection areas;

[0104] Based on the light-blocking decorative area, the optimized three-dimensional decorative unit is reconstructed to obtain candidate reconstructed three-dimensional decorative units;

[0105] The load-bearing capacity of the candidate reconstructed 3D decorative units is self-checked, and the load-bearing capacity of the candidate reconstructed 3D decorative units is screened for compliance based on the self-checked load-bearing capacity to obtain the final 3D decorative units.

[0106] In this embodiment, light scattering trajectory data from the light source is imported. This data includes spatial coordinate sequences, direction vectors, and timestamp information. The trajectory points are normalized according to a unified three-dimensional coordinate system, adjusting the trajectory coordinates to the local coordinate system of the decorative unit in the simulation space. A three-dimensional geometric transformation matrix is ​​used to complete the coordinate system transformation, including translation and rotation matrices, ensuring that the trajectory and the vertex coordinates of the three-dimensional decorative unit model are in the same coordinate system. A projection matrix is ​​used to orthogonally or through perspective project the trajectory points and the vertices of the decorative unit model surface, constructing a combined projection framework. This framework uses three-dimensional space as a basis to generate a hybrid data structure containing a set of trajectory points and a set of decorative unit facets, specifically composed of point cloud data and polygon mesh data. This data is imported into a graphics processing unit (GPU) for parallel computation optimization, ultimately generating a high-precision projection matching framework file. A spatial overlap detection algorithm is called to compare the projected images of the trajectory point set and the decorative unit polygon mesh pixel by pixel. Rasterization technology is used to render the three-dimensional trajectory and the decorative unit model into two-dimensional projected images, and a pixel-level occlusion culling method is used. To filter invalid points, the intersection of the projected trajectory points and the projected decorative unit areas is calculated. A Monte Carlo integral method based on 2D space is used to estimate the probability density of the overlapping pixel areas. Combined with depth buffer (Z-buffer) information, points that overlap but have inconsistent depths are excluded. This achieves accurate identification of the intersection volume of the light source trajectory and the decorative unit in 3D space. The set of boundary polygons of the overlapping areas and their corresponding spatial index data structure are output and stored as a projection overlapping area annotation file. The boundary polygon information of the projection overlapping areas is mapped back to the 3D decorative unit surface. Using an inverse projection algorithm, the 2D projection coordinates are converted to 3D surface coordinates. Combined with the vertex normal vector information of the decorative unit mesh, vertices within the mapped area are filtered. The filtering condition is that the vertex position must fall inside the polygon of the overlapping area and the angle between its normal vector direction and the light source direction must not exceed 30 degrees. The local surface curvature and material properties of the selected vertices are calculated. A region partitioning model based on vertex weights is established. Vertices meeting the conditions are classified as light-blocking decorative areas, and their attributes are marked as specular blocking. Graph cut is then used to... The Cut algorithm refines the boundary of the light-blocking area, ensuring smooth edges, and finally generates a model data file of the light-blocking decoration area, which includes vertex indices, attribute labels and boundary description parameters. Using a structural optimization tool based on finite element analysis (FEA), the light-blocking decoration area is meshed, and the side length of the subdivided mesh is set to 0.A multi-layered mesh is constructed using a 5mm mesh. For each sub-unit, material elastic modulus and density parameters are defined. Load boundary conditions are input, with the load converted from the thermal radiation intensity generated by the scattering trajectory of the light source into an equivalent thermal load distribution. A topology optimization algorithm is used to adjust the material distribution within the light-blocking area. The optimization objective function includes minimum material utilization and maximum shading efficiency. Through iterative calculations, the mesh structure of the light-blocking area is automatically reconstructed, forming candidate 3D decorative element models that meet both load-bearing and optical performance requirements. The model file uses a high-precision mesh file format (such as STL or OBJ), with detailed mesh node coordinates and element connection relationships. The high-precision mesh model of the candidate reconstructed 3D decorative element is then loaded. Combining material mechanical property parameters, the structural mechanics analysis module is invoked, and the finite element method is used to apply gravity loads and external environmental mechanical loads to the reconstructed units. Load boundary conditions include a gravitational acceleration of 9.81 m / s², with the load application point covering the maximum outward expansion of the decorative unit. The stress-strain distribution within the unit is calculated, and the Von Mises stress criterion is applied to assess structural safety. The structural safety margin threshold is set to 1.5 times the ultimate strength. The maximum stress concentration area is automatically identified and a safety report is generated. All reconstructed units meeting the safety margin requirements are marked as load-bearing qualified. A batch processing system is used to filter candidate models, outputting the final set of 3D decorative unit models that have passed the load-bearing qualification screening.

[0107] Preferably, the integration of the three-dimensional light source unit and the final three-dimensional decoration unit in step S5 includes:

[0108] The final 3D decorative unit is projected into 3D space to obtain the projected 3D decorative unit;

[0109] The projection position data of the three-dimensional light source unit is determined based on the physical connection relationship of the lighting equipment and the projection three-dimensional decorative unit;

[0110] Based on the projection position data, the three-dimensional light source unit is projected onto the projection three-dimensional decorative unit, thereby integrating it into a lighting equipment design integration model.

[0111] In this embodiment, high-precision model data of 3D decorative units that have passed load-bearing qualification screening is imported. This model data includes vertex coordinate sets, polygon mesh connection information, and material attribute labels. Based on the overall 3D simulation space coordinate system of the lighting equipment, the decorative unit model is spatially positioned by constructing a transformation matrix. The specific transformations include translation and rotation transformation matrices. The translation vector is determined based on the coordinates of the decorative unit's design and installation reference point, and the rotation angle is calculated based on the angle parameters of the decorative unit relative to the main structure of the lighting fixture in the design drawings. The transformation matrix is ​​applied to all vertex coordinates to complete the model's spatial repositioning. Subsequently, a visual mesh structure of the projected 3D decorative units is generated by the rendering engine. This mesh structure file is stored in OBJ or FBX format and contains the projected vertex coordinates and updated face indexes, ensuring that all 3D data is completely mapped to a unified space. The structural connection data of the lighting equipment is read. This data includes the connection point coordinates, connection direction vectors, and connection types of each functional component. The spatial distribution relationship of the connection points is analyzed, and a relationship diagram between the connection points is established using topological analysis methods. Combined with the spatial coordinate range of the projected 3D decorative units, the preset connection points of the light source units in the connection relationship are accurately matched, and spatial constraint optimization is applied. The method adjusts the position vector of the light source unit to ensure that the spatial error between its connection point and the corresponding connection point of the decorative unit does not exceed 0.1 mm. Combined with a rotation matrix, the posture of the light source unit is adjusted to ensure that the connection direction conforms to the equipment design parameters. After calculation, a projection position data file of the light source unit is generated, including the coordinates, posture angle, and connection constraint parameters of the light source unit. A unified coordinate system is used to normalize the data to ensure consistency with the spatial positioning of the projected 3D decorative unit. A high-precision model file of the 3D light source unit is loaded, containing vertex data, polygonal patches, and light source material parameters. A spatial transformation matrix is ​​used to transform the vertex coordinates of the light source unit to the unified 3D simulation space of the lighting equipment. Based on the coordinates and posture parameters in the projection position data, a complete rigid body transformation matrix is ​​constructed, and the vertices of the light source unit model are transformed point by point to ensure that the spatial position of the light source unit model is completely aligned with the projected 3D decorative unit. Subsequently, the possible geometric overlap areas on the surfaces of the light source unit and the decorative unit are calculated, and Boolean operation algorithms are used to merge the overlapping parts to form a unified integrated model. Finally, a 3D model fusion tool is used to generate the integrated design model of the lighting equipment. The model file format supports STEP or IGES and contains complete geometry, connection relationships, and material information.

[0112] The present invention also provides an integrated design system for a three-dimensional simulation model of lighting equipment, for executing the integrated design method for a three-dimensional simulation model of lighting equipment as described above. The integrated design system for a three-dimensional simulation model of lighting equipment includes:

[0113] The component modeling module is used to acquire basic data of lighting equipment; classify the basic data of lighting equipment according to each component to obtain data of each type of component; and perform 3D reconstruction of independent components through the data of each type of component to obtain the 3D basic building blocks of the lighting fixture for each component.

[0114] The connection identification module is used to obtain the physical connection relationship of the lighting equipment; determine the functional positioning of each unit in the three-dimensional lighting basic component unit through the physical connection relationship of the lighting equipment; and divide the three-dimensional lighting basic component unit into three-dimensional decoration unit and three-dimensional light source unit based on the functional positioning of each unit.

[0115] The structural optimization module is used to confirm the physical support structure of the lamp based on the three-dimensional decorative unit; it performs weight aggravation simulation through the basic data of the lamp equipment, and optimizes the load-bearing margin of the physical support structure of the lamp based on the simulated weight aggravation, thereby generating an optimized three-dimensional decorative unit;

[0116] The light simulation module is used to simulate light scattering from a luminaire based on a 3D light source unit to generate simulated light scattering data; and to track the light scattering trajectory of the light source based on the simulated light scattering data.

[0117] The fusion module is used to optimize the light source effect of the optimized three-dimensional decorative unit based on the light scattering trajectory of the light source, thereby generating the final three-dimensional decorative unit; the three-dimensional light source unit and the final three-dimensional decorative unit are integrated to obtain the integrated design model of the lighting equipment.

[0118] This invention, through the implementation of a component modeling module, achieves efficient acquisition and classification of basic data for lighting equipment, ensuring the systematic nature and completeness of various component data. The 3D reconstruction of independent components provides an intuitive visual expression, facilitating in-depth analysis of the lighting structure by designers. The introduction of a connection identification module ensures accurate identification of the physical connections between the components of the lighting fixture, thus clearly defining the functional positioning of each unit. The separation of 3D decorative units and 3D light source units provides a clear structural foundation for subsequent design. The structural optimization module, through weight intensification simulation, optimizes the load-bearing capacity of the physical support structure of the lighting fixture, ensuring its safety and stability. The generated optimized 3D decorative units not only improve load-bearing capacity but also enhance overall aesthetics and practicality. The application of the light simulation module simulates light scattering from the 3D light source units. The generated simulated light scattering data provides important empirical evidence for the optical performance of the luminaire. The process of tracing the light scattering trajectory of the light source provides accurate data support for subsequent optimization of the light source's influence. The implementation of the fusion integration module realizes the effective combination of optimizing the three-dimensional decorative unit and the light source's influence. The generated final three-dimensional decorative unit improves the luminous efficacy and lighting effect of the luminaire. The design integration model formed by integrating the three-dimensional light source unit and the final three-dimensional decorative unit not only ensures the consistency and coordination of the overall design, but also provides detailed technical basis for actual production. The implementation of the overall system improves the efficiency of luminaire design, shortens the development cycle, reduces production costs, and promotes the digitalization and intelligentization of luminaire design. It meets the modern market's demand for high-quality luminaires, enhances the product's market competitiveness and innovation capabilities, and injects new vitality and impetus into the development of the luminaire industry.

[0119] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0120] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. An integrated design method for three-dimensional simulation models of lighting equipment, characterized in that, Includes the following steps: Step S1: Obtain basic data for the lighting equipment; The basic data of lighting equipment is categorized according to each component to obtain data for each component. Independent 3D reconstruction of various component data is performed to obtain the basic 3D lighting fixture components of each component. Step S2: Obtain the physical connection relationship of the lighting equipment; The functional positioning of each unit in the basic three-dimensional lighting fixture is determined by the physical connection relationship of the lighting equipment; based on the functional positioning of each unit, the basic three-dimensional lighting fixture is divided into three-dimensional decorative units and three-dimensional light source units. Step S3: Confirm the physical support structure of the lighting fixture based on the 3D decorative unit; perform weight amplification simulation using the basic data of the lighting fixture equipment, and optimize the load-bearing margin of the physical support structure of the lighting fixture based on the simulated amplified weight, thereby generating an optimized 3D decorative unit; Step S3, which involves performing weight amplification simulation using the basic data of the lighting fixture equipment and optimizing the load-bearing margin of the physical support structure of the lighting fixture based on the simulated amplified weight, includes: Obtain the weight data of the lighting fixture's additional components; Extract the overall weight data of the lighting fixtures from the basic data of the lighting equipment; By using the weight data of the lamp's additional components to simulate the overall weight of the lamp, the simulated weight increase is obtained. Stress response simulation is performed on the physical support structure of the lighting fixture to generate load-bearing capacity data of the lighting fixture support structure; Interactive analysis is performed based on simulated weight and load-bearing capacity data to determine the load-bearing capacity margin; the interactive analysis includes: Node alignment processing is performed on the simulated weight and load-bearing capacity data to generate node weight comparison data; Local load distribution screening is performed based on node weight comparison data to obtain distribution screening data; Mark the extreme states of the distribution screening data and generate extreme state data; Calculate the load-bearing margin of the limit state data to obtain the load-bearing margin data; The load-bearing margin data is integrated into a full-domain margin to obtain the load-bearing requirement margin. Based on the load-bearing requirements and margin, the physical support structure of the lighting fixture is optimized to generate an optimized three-dimensional decorative unit. Step S4: Simulate light scattering from the luminaire based on the three-dimensional light source unit to generate simulated light scattering data; track the light scattering trajectory of the light source based on the simulated light scattering data; Step S5: Optimize the light source influence of the optimized 3D decorative unit based on the light scattering trajectory of the light source to generate the final 3D decorative unit; integrate the 3D light source unit and the final 3D decorative unit to obtain the integrated design model of the lighting equipment.

2. The integrated design method for three-dimensional simulation models of lighting equipment according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain basic data of lighting equipment; perform multi-dimensional attribute annotation on the basic data of lighting equipment, and deconstruct the structural hierarchy of the basic data of lighting equipment based on the attribute annotation data to obtain the component hierarchy relationship; Step S12: Map the component hierarchy to the lighting equipment basic data, and classify the lighting equipment basic data into component categories according to the component hierarchy to obtain various component data. The number of component categories shall not be less than 4, including housing components, light source components, power supply components and control components. Step S13: Perform 3D projection of each category using various component data to obtain the framework of each category component, wherein the 3D projection uses a voxel accuracy of 0.1-1.0mm for contour reconstruction; Step S14: Based on the data of various components, assemble the framework of each category of components into three-dimensional units to generate the three-dimensional basic components of the lighting fixture. The assembly size tolerance is controlled within 0.3mm, and the component volume is limited to 20-800cm³.

3. The integrated design method for three-dimensional simulation models of lighting equipment according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Obtain the physical connection relationship of the lighting equipment; confirm the function of each connection point based on the physical connection relationship of the lighting equipment; Step S22: Virtually project the three-dimensional lighting fixture basic component units onto each lighting fixture point in the physical connection relationship of the lighting equipment. The projection range is: projection angle less than 30°, 30° to 60°, 60° to 90°, and the projection accuracy requirement is an error range of less than 1mm. Step S23: Map the locations of each lighting equipment based on the function of each connection point to obtain the functional positioning of each unit in the three-dimensional lighting fixture basic component unit; Step S24: Based on the functional positioning of each unit, the basic unit of the lighting equipment is spatially divided to obtain a three-dimensional decorative unit and a three-dimensional light source unit. The volume range of each unit is 1-1000cm³ for the three-dimensional decorative unit and 10-500cm³ for the three-dimensional light source unit.

4. The integrated design method for three-dimensional simulation models of lighting equipment according to claim 1, characterized in that, In step S3, the physical support structure of the lighting fixture is confirmed based on the three-dimensional decorative unit, including: Identify the unit geometry of three-dimensional decorative elements; Fit the unit geometry to the three-dimensional edge contour of the decorative unit; The three-dimensional edge contour is locally sectioned to obtain the decorative unit section frame; The centroid data of the three-dimensional decorative unit are analyzed based on the cross-sectional frame of the decorative unit. Extract material composition data from the 3D decorative unit; Material distribution simulation is performed based on material composition data and decorative unit cross-sectional framework to generate decorative unit material distribution data; The unit centroid data and the decorative unit material distribution data are coupled and processed to infer the mechanical centroid of the decorative unit; Gravity conduction is traced by the mechanical center of gravity of the decorative unit to generate the force conduction path of the lamp; Reconstruct the physical support structure of the luminaire based on the force transmission path of the luminaire.

5. The integrated design method for three-dimensional simulation models of lighting equipment according to claim 1, characterized in that, Step S4, which involves simulating light scattering from the luminaire based on a three-dimensional light source unit, includes: Constructing the light emission domain of a light source based on three-dimensional light source units; Light scattering nodes are traced based on the light emission domain of the light source to generate multi-directional light scattering nodes; The luminous intensity of the light source's emission domain is encoded to obtain the luminous intensity distribution code; By embedding the luminous intensity distribution code into the multi-directional light scattering node, simulated light scattering data is obtained.

6. The integrated design method for three-dimensional simulation models of lighting equipment according to claim 1, characterized in that, Step S4, which involves tracing the light scattering trajectory of the light source based on simulated light scattering data, includes: The starting point of the simulated light scattering data is marked to obtain the starting label of the scattered light; Based on the structured light path vector of the scattered light starting tag, an initial light path vector is generated; The energy transfer path is obtained by mapping the energy transfer between scattering nodes of the simulated light scattering data using the initial vector of the light path; The energy transfer path data is fused and recombined to generate a fused scattering path; The temporal trajectory is reconstructed based on the fused scattering path, thereby obtaining the scattering trajectory of the light source.

7. The integrated design method for three-dimensional simulation models of lighting equipment according to claim 1, characterized in that, Step S5 involves optimizing the light source influence on the 3D decorative unit based on the light scattering trajectory of the light source, including: By combining the light scattering trajectory of the light source and optimizing the three-dimensional decorative units, they are projected into the same space to obtain a combined projection frame; Based on the identification of light scattering trajectories from the light source using a combined projection frame and the optimization of the overlapping projection areas of the 3D decorative units; Optimize the light-blocking decorative area in the 3D decorative unit by mapping the overlapping projection areas; Based on the light-blocking decorative area, the optimized three-dimensional decorative unit is reconstructed to obtain candidate reconstructed three-dimensional decorative units; The load-bearing capacity of the candidate reconstructed 3D decorative units is self-checked, and the load-bearing capacity of the candidate reconstructed 3D decorative units is screened for compliance based on the self-checked load-bearing capacity to obtain the final 3D decorative units.

8. The integrated design method for three-dimensional simulation models of lighting equipment according to claim 1, characterized in that, Step S5 integrates the three-dimensional light source unit and the final three-dimensional decoration unit, including: The final 3D decorative unit is projected into 3D space to obtain the projected 3D decorative unit; The projection position data of the three-dimensional light source unit is determined based on the physical connection relationship of the lighting equipment and the projection three-dimensional decorative unit; Based on the projection position data, the three-dimensional light source unit is projected onto the projection three-dimensional decorative unit, thereby integrating it into a lighting equipment design integration model.

9. An integrated design system for three-dimensional simulation models of lighting equipment, characterized in that, For executing the integrated design method for a three-dimensional simulation model of a lighting equipment as described in claim 1, the integrated design system for the three-dimensional simulation model of a lighting equipment includes: The component modeling module is used to acquire basic data of lighting equipment; classify the basic data of lighting equipment according to each component to obtain data of each type of component; and perform 3D reconstruction of independent components through the data of each type of component to obtain the 3D basic building blocks of the lighting fixture for each component. The connection identification module is used to obtain the physical connection relationship of the lighting equipment; determine the functional positioning of each unit in the three-dimensional lighting basic component unit through the physical connection relationship of the lighting equipment; and divide the three-dimensional lighting basic component unit into three-dimensional decoration unit and three-dimensional light source unit based on the functional positioning of each unit. The structural optimization module is used to confirm the physical support structure of the lamp based on the three-dimensional decorative unit; it performs weight aggravation simulation through the basic data of the lamp equipment, and optimizes the load-bearing margin of the physical support structure of the lamp based on the simulated weight aggravation, thereby generating an optimized three-dimensional decorative unit; The light simulation module is used to simulate light scattering from a luminaire based on a 3D light source unit to generate simulated light scattering data; and to track the light scattering trajectory of the light source based on the simulated light scattering data. The fusion module is used to optimize the light source effect of the optimized three-dimensional decorative unit based on the light scattering trajectory of the light source, thereby generating the final three-dimensional decorative unit; the three-dimensional light source unit and the final three-dimensional decorative unit are integrated to obtain the integrated design model of the lighting equipment.

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